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Vision-language models (VLMs) have shown remarkable success across various multi-modal tasks, yet large VLMs encounter significant efficiency challenges due to processing numerous visual tokens.
Generation and comprehension of unambiguous object descriptions
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Learning to weight samples for dynamic early-exiting networks
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Language models (mostly) know what they know
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Large language model cascades with mixture of thoughts representations for cost-efficient reasoning
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Sigmoid loss for language image pre-training
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Phi-3 technical report: A highly capable language model locally on your phone
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Llama-vid: An image is worth 2 tokens in large language models
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Mmbench: Is your multi-modal model an all-around player?
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